Both of the above mentioned approaches have been found helpful in various medical semantic segmentation applications [1]}, [2]}, [3]}, [4]}. However, both UDA strategies require direct access to the source domain data to compute their corresponding loss functions.
To relax this requirement, UDA has been recently explored in a source-free setting in order to address applications in which the source domain is not directly accessible [5]}, [6]}.
Both Kundu et al. [5]} and Saltori et al. [6]} target image classification, and benefit from generative adversarial learning to generate pseudo-data points that are similar to the source domain data in the absence of actual source samples. While both approaches are suitable for classification problems, extending them to semantic segmentation of medical images is not trivial. First, training models that can generate realistic medical images is considerably more challenging due to importance of fine details. Second, one may argue that if the generated images are too similar to the real images, the privacy of human subjects in the training data may still be compromised. Our work is based on using a dramatically different approach. We develop a source-free UDA algorithm that performs the distribution alignment of two domains in an embedding space by using an intermediate prototypical distribution to relax the need for source data.
